{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "Declare all imports"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "from collections import Counter\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from src.dataobjs.cluster import Clusters\n",
    "from src.dataobjs.mention_data import MentionData\n",
    "from src.utils.io_utils import write_mention_to_json\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Measure Clusters and Mentions by type "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "dev_mentions = MentionData.read_mentions_json_to_mentions_data_list(\n",
    "            '../resources/wec/dev/Event_gold_mentions.json')\n",
    "\n",
    "test_mentions = MentionData.read_mentions_json_to_mentions_data_list(\n",
    "            '../resources/wec/test/Event_gold_mentions.json')\n",
    "\n",
    "train_mentions = MentionData.read_mentions_json_to_mentions_data_list(\n",
    "            '../resources/wec/train/Event_gold_mentions.json')\n",
    "\n",
    "all_mentions = test_mentions\n",
    "\n",
    "found = False\n",
    "final_mentions = list()\n",
    "all_types = dict()\n",
    "for mention in all_mentions:\n",
    "    if mention.mention_type not in all_types:\n",
    "        all_types[mention.mention_type] = dict()\n",
    "    if mention.coref_chain not in all_types[mention.mention_type]:\n",
    "        all_types[mention.mention_type][mention.coref_chain] = 0\n",
    "    all_types[mention.mention_type][mention.coref_chain] += 1\n",
    "\n",
    "for _type in all_types.keys():\n",
    "    print(str(_type) + \"-Clusters=\" + str(len(all_types[_type])))\n",
    "    print(str(_type) + \"-Mentions=\" + str(sum([clust for clust in all_types[_type].values()])))\n",
    "print(\"Done-\" + str(all_types))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Draw histograms of the data\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 13, 14, 14, 14, 14, 15, 17, 18, 18, 18, 20, 20, 23, 26, 33]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x540 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family' : 'sans-serif',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 22}\n",
    "\n",
    "mentions = MentionData.read_mentions_json_to_mentions_data_list(\n",
    "                       '../resources/wec/dev/Event_gold_mentions.json')\n",
    "clusters = Clusters.from_mentions_to_gold_clusters(mentions)\n",
    "\n",
    "\n",
    "data = [len(clust) for clust in clusters.values() if len(clust) <= 80] \n",
    "print(str(sorted(data)))\n",
    "bins = np.arange(min(data), max(data) + 1, 1)\n",
    "\n",
    "plt.rc('font', **font)\n",
    "plt.rcParams.update({'text.color' : \"white\", 'axes.labelcolor' : \"white\"})\n",
    "plt.rcParams.update({'xtick.color':'white'})\n",
    "\n",
    "plt.xlim([0, max(data)])\n",
    "\n",
    "plt.hist(data, bins=bins)\n",
    "plt.title('TRAIN')\n",
    "plt.xlabel('Cluster Size')\n",
    "plt.ylabel('Count')\n",
    "plt.rcParams['figure.figsize'] = [15, 7.5]\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}